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GAIA – Open-source framework for building AI agents that run on local hardware

amd-gaia.ai

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Re: GAIA – Open-source framework for building AI agents that run on local hardware

#41
post #36

Earlier quoted context omitted.

A couple 5060s and a couple 3060s. They are wired via PCI risers to an older mono with an amd cpu. (I wanted to avoid long 3-fan cards.) It looks like a mining rig, but with thicker pci risers. Many llm tools easily leverage multiple GPUs. Sucks 800w at full load, idles below 50w.

I have three 3090 cards. Are you saying you run them together using specialized hardware or can I somehow combine them using software over Ethernet?

If you have either PCIe slots or risers you can put them in the one system.

llama.cpp will let you run inference remotely across different systems but I suspect this would be far too latent to be worthwhile. If you have three systems already then it would cost you a few minutes to test it.

Re: GAIA – Open-source framework for building AI agents that run on local hardware

#42

ROCm is finally getting better due to a few well meaning engineers. But let’s be honest, AMD has been an extremely bad citizen to non-corporate users. For my iGPU I have to fake GFX900 and build things from source or staging packages to get that working. Support for GFX90c is finally in the pipeline… The improvements feel like a bodyguard finally letting you through the door just because NVIDIA is eating their lunch…

Debian build their ROCm with support for all possible devices. If you are tired of compiling from source just use a Debian Stable container, install their libraries in your container build, and pass /dev/kfd and /dev/dri to the container. No ROCm or out-of-tree drivers required on the container host, just regular upstream Linux kernel amdgpu and those two devices to the container.

It's also probably worth trying Vulkan inference. It is now faster than ROCm - both tg and pp over 16k ctx - on Strix Halo so maybe you'll see the benefits too.

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